一种基于活动轮廓模型的PET-CT肺肿瘤分割方法

针对PET-CT肺肿瘤分割中存在的没有充分将医生临床经验融入到算法设计的问题,该文利用PET高斯分布先验,结合区域可伸缩拟合(RSF)模型和最大似然比分类(MLC)准则,提出一种基于变分水平集的混合活动轮廓模型RSF_ML。进一步,借鉴人工勾画肺肿瘤过程中融合图像的重要价值,提出了基于RSF_ML的PET-CT肺肿瘤融合图像分割方法。实验表明,所提出方法较好地实现了有代表性的非小细胞肺肿瘤(Non-Small Cell Lung Cancer, NSCLC)的精确分割,主客观结果优于对比方法,可为临床提供有效的计算机辅助分割结果。     

Simultaneous nonrigid registration, segmentation, and tumor detection in MRI guided cervical cancer radiation therapy

External beam radiation therapy (EBRT) for the treatment of cancer enables accurate placement of radiation dose on the cancerous region. However, the deformation of soft tissue during the course of treatment, such as in cervical cancer, presents significant challenges for the delineation of the target volume and other structures of interest. Furthermore, the presence and regression of pathologies such as tumors may violate registration constraints and cause registration errors. In this paper, automatic segmentation, nonrigid registration and tumor detection in cervical magnetic resonance (MR) data are addressed simultaneously using a unified Bayesian framework. The proposed novel method can generate a tumor probability map while progressively identifying the boundary of an organ of interest based on the achieved nonrigid transformation. The method is able to handle the challenges of significant tumor regression and its effect on surrounding tissues. The new method was compared to various currently existing algorithms on a set of 36 MR data from six patients, each patient has six T2-weighted MR cervical images. The results show that the proposed approach achieves an accuracy comparable to manual segmentation and it significantly outperforms the existing registration algorithms. In addition, the tumor detection result generated by the proposed method has a high agreement with manual delineation by a qualified clinician.     

Real-time volume rendering visualization of dual-modality PET/CT images with interactive fuzzy thresholding segmentation.

Three-dimensional (3-D) visualization has become an essential part for imaging applications, including image-guided surgery, radiotherapy planning, and computer-aided diagnosis. In the visualization of dual-modality positron emission tomography and computed tomography (PET/CT), 3-D volume rendering is often limited to rendering of a single image volume and by high computational demand. Furthermore, incorporation of segmentation in volume rendering is usually restricted to visualizing the presegmented volumes of interest. In this paper, we investigated the integration of interactive segmentation into real-time volume rendering of dual-modality PET/CT images. We present and validate a fuzzy thresholding segmentation technique based on fuzzy cluster analysis, which allows interactive and real-time optimization of the segmentation results. This technique is then incorporated into a real-time multi-volume rendering of PET/CT images. Our method allows a real-time fusion and interchangeability of segmentation volume with PET or CT volumes, as well as the usual fusion of PET/CT volumes. Volume manipulations such as window level adjustments and lookup table can be applied to individual volumes, which are then fused together in real time as adjustments are made. We demonstrate the benefit of our method in integrating segmentation with volume rendering in its application to PET/CT images. Responsive frame rates are achieved by utilizing a texture-based volume rendering algorithm and the rapid transfer capability of the high-memory bandwidth available in low-cost graphic hardware.     

Robust shape regression for supervised vessel segmentation and its application to coronary segmentation in CTA

This paper presents a vessel segmentation method which learns the geometry and appearance of vessels in medical images from annotated data and uses this knowledge to segment vessels in unseen images. Vessels are segmented in a coarse-to-fine fashion. First, the vessel boundaries are estimated with multivariate linear regression using image intensities sampled in a region of interest around an initialization curve. Subsequently, the position of the vessel boundary is refined with a robust nonlinear regression technique using intensity profiles sampled across the boundary of the rough segmentation and using information about plausible cross-sectional vessel shapes. The method was evaluated by quantitatively comparing segmentation results to manual annotations of 229 coronary arteries. On average the difference between the automatically obtained segmentations and manual contours was smaller than the inter-observer variability, which is an indicator that the method outperforms manual annotation. The method was also evaluated by using it for centerline refinement on 24 publicly available datasets of the Rotterdam Coronary Artery Evaluation Framework. Centerlines are extracted with an existing method and refined with the proposed method. This combination is currently ranked second out of 10 evaluated interactive centerline extraction methods. An additional qualitative expert evaluation in which 250 automatic segmentations were compared to manual segmentations showed that the automatically obtained contours were rated on average better than manual contours.     

Automatic 3-D segmentation of internal structures of the head in MR images using a combination of similarity and free-form transformations: Part I, Methodology and validation on normal subjects

The study presented in this paper tests the hypothesis that the combination of a global similarity transformation and local free-form deformations can be used for the accurate segmentation of internal structures in MR images of the brain. To quantitatively evaluate our approach, the entire brain, the cerebellum, and the head of the caudate have been segmented manually by two raters on one of the volumes (the reference volume) and mapped back onto all the other volumes, using the computed transformations. The contours so obtained have been compared to contours drawn manually around the structures of interest in each individual brain. Manual delineation was performed twice by the same two raters to test inter- and intrarater variability. For the brain and the cerebellum, results indicate that for each rater, contours obtained manually and contours obtained automatically by deforming his own atlas are virtually indistinguishable. Furthermore, contours obtained manually by one rater and contours obtained automatically by deforming this rater's own atlas are more similar than contours obtained manually by two raters. For the caudate, manual intra- and interrater similarity indexes remain slightly better than manual versus automatic indexes, mainly because of the spatial resolution of the images used in this study. Qualitative results also suggest that this method can be used for the segmentation of more complex structures, such as the hippocampus.     

A novel algorithm for computer-assisted measurement of cervical length from transvaginal ultrasound images

The cervical length measured by transvaginal ultrasound is a proven clinical tool for predicting premature birth. The standard manual measurement of the cervix is limited by variability in the technique. In this research, we develop the first computer algorithm that is able to identify the anatomic landmarks of the cervix on a transvaginal ultrasound image and determine the standard cervical length. The system is composed of four stages: The first stage is adaptive speckle suppression using variable length sticks algorithm. The second stage is the location of the internal cervical opening or "os" using a region-based segmentation. The third stage is delineation of the cervical canal. The fourth stage uses gray level summation patterns and prior knowledge to first localize the tissue boundary of the external cervix, and then use a template to determine the specific location of the external os. The cervical length is determined and calculated to image scale. To validate the proposed algorithm, 101 cervical ultrasound images were selected from a series of 37 examinations performed on 17 patients over an eight-month period. Repeated measurements of cervical length using the computer-assisted method were compared with those carried out by two experienced sonographers. The median intraobserver variability for the 101 images using the computer-assisted method was significantly smaller than that of the manual method by either sonographer. In a pairwise comparison, the mean cervical length for the computer method matches with the mean manual cervical length.     

Color- and Texture-Based Image Segmentation for Improved Forest Delineation

This paper concentrates on the delineation of forest boundaries from aerial images with focus on spatially contiguous and reproducible results for the Swiss National Forest Inventory. Because of the poor performance of common edge models to extract natural vegetation boundaries, this paper presents a combined method of image segmentation and wavelet-based texture features for the delineation of forest. The selected -measure-based segmentation method has been found to be useful to produce initial segmentation results, but lacks a semantic concept for forest vegetation. To overcome this conceptual limitation, the combination with wavelet transformation gives access to additional texture features and leads to a robust approach to obtain proper forest boundaries. Preliminary results are encouraging regarding the better agreement compared with maximum-likelihood classification results.     

Segmentation of VOI from multidimensional dynamic PET images by integrating spatial and temporal features.

Segmentation of multidimensional dynamic positron emission tomography (PET) images into volumes of interest (VOIs) exhibiting similar temporal behavior and spatial features is a challenging task due to inherently poor signal-to-noise ratio and spatial resolution. In this study, we propose VOI segmentation of dynamic PET images by utilizing both the three-dimensional (3-D) spatial and temporal domain information in a hybrid technique that integrates two independent segmentation techniques of cluster analysis and region growing. The proposed technique starts with a cluster analysis that partitions the image based on temporal similarities. The resulting temporal partitions, together with the 3-D spatial information are utilized in the region growing segmentation. The technique was evaluated with dynamic 2-[18F] fluoro-2-deoxy-D-glucose PET simulations and clinical studies of the human brain and compared with the k-means and fuzzy c-means cluster analysis segmentation methods. The quantitative evaluation with simulated images demonstrated that the proposed technique can segment the dynamic PET images into VOIs of different kinetic structures and outperforms the cluster analysis approaches with notable improvements in the smoothness of the segmented VOIs with fewer disconnected or spurious segmentation clusters. In clinical studies, the hybrid technique was only superior to the other techniques in segmenting the white matter. In the gray matter segmentation, the other technique tended to perform slightly better than the hybrid technique, but the differences did not reach significance. The hybrid technique generally formed smoother VOIs with better separation of the background. Overall, the proposed technique demonstrated potential usefulness in the diagnosis and evaluation of dynamic PET neurological imaging studies.     

Semiautomated segmentation of myocardial contours for fast strain analysis in cine displacement-encoded MRI

The purposes of this study were to develop a semiautomated cardiac contour segmentation method for use with cine displacement-encoded MRI and evaluate its accuracy against manual segmentation. This segmentation model was designed with two distinct phases: preparation and evolution. During the model preparation phase, after manual image cropping and then image intensity standardization, the myocardium is separated from the background based on the difference in their intensity distributions, and the endo- and epi-cardial contours are initialized automatically as zeros of an underlying level set function. During the model evolution phase, the model deformation is driven by the minimization of an energy function consisting of five terms: model intensity, edge attraction, shape prior, contours interaction, and contour smoothness. The energy function is minimized iteratively by adaptively weighting the five terms in the energy function using an annealing algorithm. The validation experiments were performed on a pool of cine data sets of five volunteers. The difference between the semiautomated segmentation and manual segmentation was sufficiently small as to be considered clinically irrelevant. This relatively accurate semiautomated segmentation method can be used to significantly increase the throughput of strain analysis of cine displacement-encoded MR images for clinical applications.      

Segmentation of kidney from ultrasound images based on texture and shape priors

This paper presents a novel texture and shape priors based method for kidney segmentation in ultrasound (US) images. Texture features are extracted by applying a bank of Gabor filters on test images through a two-sided convolution strategy. The texture model is constructed via estimating the parameters of a set of mixtures of half-planed Gaussians using the expectation-maximization method. Through this texture model, the texture similarities of areas around the segmenting curve are measured in the inside and outside regions, respectively. We also present an iterative segmentation framework to combine the texture measures into the parametric shape model proposed by Leventon and Faugeras. Segmentation is implemented by calculating the parameters of the shape model to minimize a novel energy function. The goal of this energy function is to partition the test image into two regions, the inside one with high texture similarity and low texture variance, and the outside one with high texture variance. The effectiveness of this method is demonstrated through experimental results on both natural images and US data compared with other image segmentation methods and manual segmentation.     

Normalized cuts in 3-D for spinal MRI segmentation

Segmentation of medical images has become an indispensable process to perform quantitative analysis of images of human organs and their functions. Normalized Cuts (NCut) is a spectral graph theoretic method that readily admits combinations of different features for image segmentation. The computational demand imposed by NCut has been successfully alleviated with the Nystr?m approximation method for applications different than medical imaging. In this paper we discuss the application of NCut with the Nystr?m approximation method to segment vertebral bodies from sagittal T1-weighted magnetic resonance images of the spine. The magnetic resonance images were preprocessed by the anisotropic diffusion algorithm, and three-dimensional local histograms of brightness was chosen as the segmentation feature. Results of the segmentation as well as limitations and challenges in this area are presented.     

Adaptive segmentation of MRI data

Intensity-based classification of MR images has proven problematic, even when advanced techniques are used. Intrascan and interscan intensity inhomogeneities are a common source of difficulty. While reported methods have had some success in correcting intrascan inhomogeneities, such methods require supervision for the individual scan. This paper describes a new method called adaptive segmentation that uses knowledge of tissue intensity properties and intensity inhomogeneities to correct and segment MR images. Use of the expectation-maximization (EM) algorithm leads to a method that allows for more accurate segmentation of tissue types as well as better visualization of magnetic resonance imaging (MRI) data, that has proven to be effective in a study that includes more than 1000 brain scans. Implementation and results are described for segmenting the brain in the following types of images: axial (dual-echo spin-echo), coronal [three dimensional Fourier transform (3-DFT) gradient-echo T1-weighted] all using a conventional head coil, and a sagittal section acquired using a surface coil. The accuracy of adaptive segmentation was found to be comparable with manual segmentation, and closer to manual segmentation than supervised multivariant classification while segmenting gray and white matter.     

Combining low-, high-level and empirical domain knowledge for automated segmentation of ultrasonic breast lesions

Breast cancer is the most frequently diagnosed malignancy and the second leading cause of mortality in women. In the last decade, ultrasound along with digital mammography has come to be regarded as the gold standard for breast cancer diagnosis. Automatically detecting tumors and extracting lesion boundaries in ultrasound images is difficult due to their specular nature and the variance in shape and appearance of sonographic lesions. Past work on automated ultrasonic breast lesion segmentation has not addressed important issues such as shadowing artifacts or dealing with similar tumor like structures in the sonogram. Algorithms that claim to automatically classify ultrasonic breast lesions, rely on manual delineation of the tumor boundaries. In this paper, we present a novel technique to automatically find lesion margins in ultrasound images, by combining intensity and texture with empirical domain specific knowledge along with directional gradient and a deformable shape-based model. The images are first filtered to remove speckle noise and then contrast enhanced to emphasize the tumor regions. For the first time, a mathematical formulation of the empirical rules used by radiologists in detecting ultrasonic breast lesions, popularly known as the "Stavros Criteria" is presented in this paper. We have applied this formulation to automatically determine a seed point within the image. Probabilistic classification of image pixels based on intensity and texture is followed by region growing using the automatically determined seed point to obtain an initial segmentation of the lesion. Boundary points are found on the directional gradient of the image. Outliers are removed by a process of recursive refinement. These boundary points are then supplied as an initial estimate to a deformable model. Incorporating empirical domain specific knowledge along with low and high-level knowledge makes it possible to avoid shadowing artifacts and lowers the chance of confusing similar tumor like structures for the lesion. The system was validated on a database of breast sonograms for 42 patients. The average mean boundary error between manual and automated segmentation was 6.6 pixels and the normalized true positive area overlap was 75.1%. The algorithm was found to be robust to 1) variations in system parameters, 2) number of training samples used, and 3) the position of the seed point within the tumor. Running time for segmenting a single sonogram was 18 s on a 1.8-GHz Pentium machine.     

A new method for volume segmentation of PET images, based on possibility theory

18F-fluorodeoxyglucose positron emission tomography (18FDG PET) has become an essential technique in oncology. Accurate segmentation and uptake quantification are crucial in order to enable objective follow-up, the optimization of radiotherapy planning, and therapeutic evaluation. We have designed and evaluated a new, nearly automatic and operator-independent segmentation approach. This incorporated possibility theory, in order to take into account the uncertainty and inaccuracy inherent in the image. The approach remained independent of PET facilities since it did not require any preliminary calibration. Good results were obtained from phantom images [percent error =18.38% (mean) ± 9.72% (standard deviation)]. Results on simulated and anatomopathological data sets were quantified using different similarity measures and showed the method was efficient (simulated images: Dice index =82.18% ± 13.53% for SUV =2.5 ). The approach could, therefore, be an efficient and robust tool for uptake volume segmentation, and lead to new indicators for measuring volume of interest activity.     

一种全新的两步自动化医学图像分割方案

针对现有图像分割方法存在需要手动分割,以及精确度较低的问题。采用一种全新的两步图像分割方案。该方案。以基于人工神经网络的模式识别技术,即人工神经网络的大规模培训的方法,通过对肺区不同子区域内结构进行分割处理,利用训练好的大规模人工神经网络对标准胸片中的肋骨、锁骨等骨质结构进行抑制,结合以基于区域的活动轮廓模型,即Snake模型,正确分割亮度不均匀的图像。文中选择与医护人员人工分割的图像进行对比,通过放射科医生采用等级法打分,原图的平均分为20分,而通过文中改进的分割方法平均分高达34分。     

一种基于多层CT影像的肺部结节分割方法

本文针对多层CT影像中肺部结节分割问题，提出一种交互式手动分割结节的方法，用Canny算子代替传统Live-Wire算法中的拉普拉斯算子，解决了拉普拉斯算子边缘检测存在伪边缘和边缘间断的问题，实验证明利用该方法可以快速的为医生分割出感兴趣的结节区域，为定量分析和三维重建提供了前提，从分割和三维可视化的结果来看，该分割方法的分割结果是较为理想的．     

Automatic segmentation of high-throughput RNAi fluorescent cellular images.

High-throughput genome-wide RNA interference (RNAi) screening is emerging as an essential tool to assist biologists in understanding complex cellular processes. The large number of images produced in each study make manual analysis intractable; hence, automatic cellular image analysis becomes an urgent need, where segmentation is the first and one of the most important steps. In this paper, a fully automatic method for segmentation of cells from genome-wide RNAi screening images is proposed. Nuclei are first extracted from the DNA channel by using a modified watershed algorithm. Cells are then extracted by modeling the interaction between them as well as combining both gradient and region information in the Actin and Rac channels. A new energy functional is formulated based on a novel interaction model for segmenting tightly clustered cells with significant intensity variance and specific phenotypes. The energy functional is minimized by using a multiphase level set method, which leads to a highly effective cell segmentation method. Promising experimental results demonstrate that automatic segmentation of high-throughput genome-wide multichannel screening can be achieved by using the proposed method, which may also be extended to other multichannel image segmentation problems.     

Tracking monotonically advancing boundaries in image sequences using graph cuts and recursive kernel shape priors

We introduce a probabilistic computer vision technique to track monotonically advancing boundaries of objects within image sequences. Our method incorporates a novel technique for including statistical prior shape information into graph-cut based segmentation, with the aid of a majorization-minimization algorithm. Extension of segmentation from single images to image sequences then follows naturally using sequential Bayesian estimation. Our methodology is applied to two unrelated sets of real biomedical imaging data, and a set of synthetic images. Our results are shown to be superior to manual segmentation.     

Hierarchical scale-based multiobject recognition of 3-D anatomical structures

Segmentation of anatomical structures from medical images is a challenging problem, which depends on the accurate recognition (localization) of anatomical structures prior to delineation. This study generalizes anatomy segmentation problem via attacking two major challenges: 1) automatically locating anatomical structures without doing search or optimization, and 2) automatically delineating the anatomical structures based on the located model assembly. For 1), we propose intensity weighted ball-scale object extraction concept to build a hierarchical transfer function from image space to object (shape) space such that anatomical structures in 3-D medical images can be recognized without the need to perform search or optimization. For 2), we integrate the graph-cut (GC) segmentation algorithm with prior shape model. This integrated segmentation framework is evaluated on clinical 3-D images consisting of a set of 20 abdominal CT scans. In addition, we use a set of 11 foot MR images to test the generalizability of our method to the different imaging modalities as well as robustness and accuracy of the proposed methodology. Since MR image intensities do not possess a tissue specific numeric meaning, we also explore the effects of intensity nonstandardness on anatomical object recognition. Experimental results indicate that: 1) effective recognition can make the delineation more accurate; 2) incorporating a large number of anatomical structures via a model assembly in the shape model improves the recognition and delineation accuracy dramatically; 3) ball-scale yields useful information about the relationship between the objects and the image; 4) intensity variation among scenes in an ensemble degrades object recognition performance.     

Medical image segmentation by combining graph cuts and oriented active appearance models

In this paper, we propose a novel method based on a strategic combination of the active appearance model (AAM), live wire (LW), and graph cuts (GCs) for abdominal 3-D organ segmentation. The proposed method consists of three main parts: model building, object recognition, and delineation. In the model building part, we construct the AAM and train the LW cost function and GC parameters. In the recognition part, a novel algorithm is proposed for improving the conventional AAM matching method, which effectively combines the AAM and LW methods, resulting in the oriented AAM (OAAM). A multiobject strategy is utilized to help in object initialization. We employ a pseudo-3-D initialization strategy and segment the organs slice by slice via a multiobject OAAM method. For the object delineation part, a 3-D shape-constrained GC method is proposed. The object shape generated from the initialization step is integrated into the GC cost computation, and an iterative GC-OAAM method is used for object delineation. The proposed method was tested in segmenting the liver, kidneys, and spleen on a clinical CT data set and also on the MICCAI 2007 Grand Challenge liver data set. The results show the following: 1) The overall segmentation accuracy of true positive volume fraction TPVF > 94.3% and false positive volume fraction can be achieved; 2) the initialization performance can be improved by combining the AAM and LW; 3) the multiobject strategy greatly facilitates initialization; 4) compared with the traditional 3-D AAM method, the pseudo-3-D OAAM method achieves comparable performance while running 12 times faster; and 5) the performance of the proposed method is comparable to state-of-the-art liver segmentation algorithm. The executable version of the 3-D shape-constrained GC method with a user interface can be downloaded from http://xinjianchen.wordpress.com/research/.